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GPT as Psychologist? Preliminary Evaluations for GPT-4V on Visual Affective Computing

GPT as Psychologist? Preliminary Evaluations for GPT-4V on V...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Hao Lu Xuesong Niu Jiyao Wang Yin Wang Qingyong Hu Jiaqi Tang Yuting Zhang Kaishen Yuan Bin Huang Zitong Yu Dengbo He Shuiguang Deng Hao Chen Yingcong Chen Shiguang Shan The Hong Kong University of Science & Technology (Guangzhou) The Hong Kong University of Science & Technology Beijing Institute for General Artificial Intelligence Zhejiang University Great Bay University Hangzhou Research Institute Beihang University Chinese Academy of Sciences Institute of Computing Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Multimodal large language models (MLLMs) are designed to process and integrate information from multiple sources, such as text, speech, images, and videos. Despite its success in language understanding, it is critical to evaluate the performance of downstream tasks for better human-centric applications. This paper assesses the application of MLLMs with 5 crucial abilities for affective computing, spanning from visual affective tasks and reasoning tasks. The results show that GPT-4V has high accuracy in facial action unit recognition and micro-expression detection while its general facial expression recognition performance is not accurate. We also highlight the challenges of achieving fine-grained micro-expression recognition and the potential for further study and demonstrate the versatility and potential of GPT-4V for handling advanced tasks in emotion recognition and related fields by integrating with task-related agents for more complex tasks, such as heart rate estimation through signal processing. In conclusion, this paper provides valuable insights into the potential applications and challenges of MLLMs in human-centric computing. Our interesting examples are at https://***/Envision-Research/GPT4Affectivity.

关键词： Heart rate Emotion recognition Visualization computer vision Affective computing Accuracy Face recognition

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HyperLeaf2024 – A Hyperspectral Imaging Dataset for Classification and Regression of Wheat Leaves

HyperLeaf2024 – A Hyperspectral Imaging Dataset for Classif...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： William Michael Laprade Pawel Pieta Svetlana Kutuzova Jesper Cairo Westergaard Mads Nielsen Svend Christensen Anders Bjorholm Dahl Department of Applied Mathematics and Computer Science Technical University of Denmark Department of Computer Science University of Copenhagen Department of Plant and Environmental Sciences University of Copenhagen

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Hyperspectral imaging is a widely used method in remote sensing, particularly for use in airborne and satellite-based land surveillance. Its versatility is, however, much larger and has also seen usage in everything ranging from food processing and surveillance to astronomy and waste sorting. It is also gaining inroads with agricultural research. With most available datasets focusing on per-pixel classification, there is, however, a potential for hyperspectral whole-image analysis, but there is a severe lack of datasets for whole-image analysis. To help fill this gap and facilitate methodological development in whole-image hyperspectral image analysis, we introduce the Hy-perLeaf2024 dataset. The dataset consists of 2410 hyper-spectral images of wheat leaves, along with associated classification and regression targets at both the leaf level and the plot level. In addition to the dataset, we also provide experiments showing the importance of pretraining and highlighting the future research direction in whole-image hyper-spectral image analysis.

关键词： Adaptation models Image analysis Image coding Surveillance conferences Focusing Distance measurement

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Improving Object Detection to Fisheye Cameras with Open-Vocabulary Pseudo-Label Approach

Improving Object Detection to Fisheye Cameras with Open-Voca...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Long Hoang Pham Quoc Pham-Nam Ho Duong Nguyen-Ngoc Tran Tai Huu-Phuong Tran Huy-Hung Nguyen Duong Khac Vu Chi Dai Tran Ngoc Doan-Minh Huynh Hyung-Min Jeon Hyung-Joon Jeon Jae Wook Jeon Department of Electrical and Computer Engineering Sungkyunkwan University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Fish-eye cameras have long been employed in traffic surveillance systems to allow for wider observation of the roads. Despite their widespread use, limited computer vision research is tailored explicitly to images captured by fish-eye cameras. The AI City Challenge 2024 - Track 4 introduces a novel fish-eye camera dataset for the 2D road object detection task. This paper proposes a framework designed to detect objects in fish-eye camera images. Our approach involves several key steps: first, we generate image data to bridge the representation gap between day and night images. Next, we leverage zero-shot open vocabulary detection to produce pseudo-labels, aiding in training supervised object detection models. Additionally, we optimize the model’s hyper-parameters and inference configuration for better performance. Finally, we apply various post-processing techniques to enhance detection performance. Our solution achieves a final F1 score of 0.6194 in the AI City Challenge 2024 - Track 4, ranking third among competing teams. The source code is available at GitHub Repo.

关键词： Training computer vision Adaptation models Vocabulary Roads Urban areas Object detection

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CUE-Net: Violence Detection Video Analytics with Spatial Cropping, Enhanced UniformerV2 and Modified Efficient Additive Attention

CUE-Net: Violence Detection Video Analytics with Spatial Cro...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Damith Chamalke Senadeera Xiaoyun Yang Dimitrios Kollias Gregory Slabaugh School of Electronic Engineering and Computer Science Queen Mary University of London UK Queen Mary’s Digital Environment Research Institute (DERI) London UK Remark AI UK Limited London UK

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this paper we introduce CUE-Net, a novel architecture designed for automated violence detection in video surveillance. As surveillance systems become more prevalent due to technological advances and decreasing costs, the challenge of efficiently monitoring vast amounts of video data has intensified. CUE-Net addresses this challenge by combining spatial Cropping with an enhanced version of the UniformerV2 architecture, integrating convolutional and self-attention mechanisms alongside a novel Modified Efficient Additive Attention mechanism (which reduces the quadratic time complexity of self-attention) to effectively and efficiently identify violent activities. This approach aims to overcome traditional challenges such as capturing distant or partially obscured subjects within video frames. By focusing on both local and global spatiotemporal features, CUE-Net achieves state-of-the-art performance on the RWF-2000 and RLVS datasets, surpassing existing methods. The source code is available at 1 .

关键词： Convolutional codes Additives Attention mechanisms Visual analytics Source coding computer architecture Video surveillance

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Impact of Pseudo Depth on Open World Object Segmentation with Minimal User Guidance

Impact of Pseudo Depth on Open World Object Segmentation wit...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Robin Schön Katja Ludwig Rainer Lienhart Chair for Machine Learning and Computer Vision University of Augsburg

Pseudo depth maps are depth map predicitions which are used as ground truth during training. In this paper we leverage pseudo depth maps in order to segment objects of classes that have never been seen during training. This renders our object segmentation task an open world task. The pseudo depth maps are generated using pretrained networks, which have either been trained with the full intention to generalize to downstream tasks (LeRes and MiDaS), or which have been trained in an unsupervised fashion on video sequences (MonodepthV2). In order to tell our network which object to segment, we provide the network with a single click on the object’s surface on the pseudo depth map of the image as input. We test our approach on two different scenarios: One without the RGB image and one where the RGB image is part of the input. Our results demonstrate a considerably better generalization performance from seen to unseen object types when depth is used. On the Semantic Boundaries Dataset we achieve an improvement from 61.57 to 69.79 IoU score on unseen classes, when only using half of the training classes during training and performing the segmentation on depth maps only.

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Generalized Single-Image-Based Morphing Attack Detection Using Deep Representations from vision Transformer

Generalized Single-Image-Based Morphing Attack Detection Usi...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Haoyu Zhang Raghavendra Ramachandra Kiran Raja Christoph Busch Norwegian University of Science and Technology Norway Darmstadt University of Applied Sciences Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Face morphing attacks have posed severe threats to Face recognition Systems (FRS), which are operated in border control and passport issuance use cases. Correspondingly, morphing attack detection algorithms (MAD) are needed to defend against such attacks. MAD approaches must be robust enough to handle unknown attacks in an open-set scenario where attacks can originate from various morphing generation algorithms, post-processing and the diversity of printers/scanners. The problem of generalization is further pronounced when the detection has to be made on a single suspected image. In this paper, we propose a generalized single-image-based MAD (S-MAD) algorithm by learning the encoding from vision Transformer (ViT) architecture. Compared to CNN-based architectures, ViT model has the advantage on integrating local and global information and hence can be suitable to detect the morphing traces widely distributed among the face region. Extensive experiments are carried out on face morphing datasets generated using publicly available FRGC face datasets. Several state-of-the-art (SOTA) MAD algorithms, including representative ones that have been publicly evaluated, have been selected and benchmarked with our ViT-based approach. Obtained results demonstrate the improved detection performance of the proposed S-MAD method on inter-dataset testing (when different data is used for training and testing) and comparable performance on intra-dataset testing (when the same data is used for training and testing) experimental protocol.

关键词： Training computer vision Image coding Protocols Statistical analysis Face recognition Digital images

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Bracketing Image Restoration and Enhancement with High-Low Frequency Decomposition

Bracketing Image Restoration and Enhancement with High-Low F...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Genggeng Chen Kexin Dai Kangzhen Yang Tao Hu Xiangyu Chen Yongqing Yang Wei Dong Peng Wu Yanning Zhang Qingsen Yan Xi’an University of Architecture and Technology Northwestern Polytechnical University University of Macau Xi’an Institute of Optics and Precision Mechanics of CAS

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In real-world scenarios, due to a series of image degradations, obtaining high-quality, clear content photos is challenging. While significant progress has been made in synthesizing high-quality images, previous methods for image restoration and enhancement often overlooked the characteristics of different degradations. They applied the same structure to address various types of degradation, resulting in less-than-ideal restoration outcomes. Inspired by the notion that high/low frequency information is applicable to different degradations, we introduce HLNet, a Bracketing Image Restoration and Enhancement method based on high-low frequency decomposition. Specifically, we employ two modules for feature extraction: shared weight modules and non-shared weight modules. In the shared weight modules, we use SCConv to extract common features from different degradations. In the non-shared weight modules, we introduce the High-Low Frequency Decomposition Block (HLFDB), which employs different methods to handle high-low frequency information, enabling the model to address different degradations more effectively. Compared to other networks, our method takes into account the characteristics of different degradations, thus achieving higher-quality image restoration.

关键词： Degradation computer vision conferences Feature extraction Image restoration pattern recognition Image enhancement

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Spatial-Temporal Graph-Based AU Relationship Learning for Facial Action Unit Detection

Spatial-Temporal Graph-Based AU Relationship Learning for Fa...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Zihan Wang Siyang Song Cheng Luo Yuzhi Zhou Shiling Wu Weicheng Xie Linlin Shen Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University School of Computing and Mathematical Sciences University of Leicester

This paper presents our Facial Action Units (AUs) detection submission to the fifth Affective Behavior Analysis in-the-wild Competition (ABAW). Our approach consists of three main modules: (i) a pre-trained facial representation encoder which produce a strong facial representation from each input face image in the input sequence; (ii) an AU-specific feature generator that specifically learns a set of AU features from each facial representation; and (iii) a spatio-temporal graph learning module that constructs a spatio-temporal graph representation. This graph representation describes AUs contained in all frames and predicts the occurrence of each AU based on both the modeled spatial information within the corresponding face and the learned temporal dynamics among frames. The experimental results show that our approach outperformed the baseline and the spatio-temporal graph representation learning allows our model to generate the best results among all ablated systems. Our model ranks at the 4th place in the AU recognition track at the 5th ABAW Competition. Our code is publicly available at https://***/wzh125/ABAW-5.

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Fake it to make it: Using synthetic data to remedy the data shortage in joint multi-modal speech-and-gesture synthesis

Fake it to make it: Using synthetic data to remedy the data ...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Shivam Mehta Anna Deichler Jim O’Regan Birger Moëll Jonas Beskow Gustav Eje Henter Simon Alexanderson KTH Royal Institute of Technology Sweden Motorica AB Sweden

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Although humans engaged in face-to-face conversation simultaneously communicate both verbally and non-verbally, methods for joint and unified synthesis of speech audio and co-speech 3D gesture motion from text are a new and emerging field. These technologies hold great promise for more human-like, efficient, expressive, and robust synthetic communication, but are currently held back by the lack of suitably large datasets, as existing methods are trained on parallel data from all constituent modalities. Inspired by student-teacher methods, we propose a straightforward solution to the data shortage, by simply synthesising additional training material. Specifically, we use uni-modal synthesis models trained on large datasets to create multi-modal (but synthetic) parallel training data, and then pre-train a joint synthesis model on that material. In addition, we propose a new synthesis architecture that adds better and more controllable prosody modelling to the state-of-the-art method in the field. Our results confirm that pre-training on large amounts of synthetic data improves the quality of both the speech and the motion synthesised by the multi-modal model, with the proposed architecture yielding further benefits when pre-trained on the synthetic data.

关键词： Training computer vision Three-dimensional displays conferences Training data computer architecture Oral communication

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Learning to See in Nighttime Driving Scenes with Inter-frequency Priors

Learning to See in Nighttime Driving Scenes with Inter-frequ...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Zhentao Fan Xianhao Wu Xiang Chen Yufeng Li College of Electronic and Information Engineering Shenyang Aerospace University School of Computer Science and Engineering Nanjing University of Science and Technology

Currently, image-to-image translation methods have achieved significant performance with the help of deep CNNs and GANs, but most existing models are not suitable for autonomous driving scenarios due to their interpretability. In this paper, we develop a high-quality image translator (i.e. night → day), N2D-LPNet, to facilitate nighttime driving scene dperception. Instead of pursuing a complex network structure, we first attempt to explore the inter-frequency relation knowledge to simplify image translation process. Specifically, the lightweight Laplace pyramid is introduced as the backbone architecture to decompose the feature maps of nighttime image into high- and low-frequency components. Considering the similar morphological properties of different frequency components, we design a flexible inter-frequency guiding strategy which utilizes each lower frequency information to refine the higher frequency feature in a progressive manner. Benefiting from the advantage of constraint from inter-frequency priors, our method can process the nighttime image better under the practical driving system while still persevere competitive results. We also discuss the potential value of N2D-LPNet for other high-level vision tasks.

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